为解决复杂环境下的无人机航迹规划问题，提出了一种多重启发蚁群优化算法．该算法综合考虑无人机当前位置与待选位置之间的距离和威胁分布，以及待选位置与目标位置之间的距离和威胁分布，将这些已知信息构造为蚂蚁状态转移的多重启发信息，指导蚂蚁的搜索行为．文中对多重启发蚁群优化算法的收敛性进行了分析，并针对航迹不可行和任务区域内存在的突发威胁，分别给出了航迹平滑方法和在线航迹再规划方法．仿真结果表明: 所提方法能够有效地增强蚁群优化算法的航迹规划能力，提高收敛的速度和精度，得到最优的飞行航迹．

In this paper,a multi-heuristic ant colony optimization algorithm is proposed for the route planning of the unmanned aerial vehicle ( UAV) in complex environments. In the algorithm,the distance and the threat distribution between the current UAV position and the candidate one,as well as between the candidate position and the target one,are designed as the multi-heuristic information in the state transition of ants to guide their search behaviors. Moreover,the convergence of the ant colony optimization algorithm is analyzed,and the route smoothing and online route replanning methods are presented respectively for the unfeasible route and the pop-up threats in the task region. Simulation results show that the proposed methods can effectively enhance the route planning ability of the ant colony optimization algorithm and improve the speed and precision of the convergence,thus achieving the optimal route.